Accurate Traffic Flow Prediction Research Articles

Adaptive Graph Convolutional Recurrent Network with Transformer and Whale Optimization Algorithm for Traffic Flow Prediction

Accurate traffic flow prediction plays a crucial role in the development of intelligent traffic management. Despite numerous investigations into spatio-temporal methods, achieving high accuracy in traffic flow prediction remains challenging. This challenge arises from the complex dynamic spatio-temporal correlations within the traffic road network and the limitations imposed by the selection of hyperparameters based on experiments and manual experience, which can affect the performance of the network architecture. This paper introduces a novel transformer-based adaptive graph convolutional recurrent network. The proposed network automatically infers the interdependencies among different traffic sequences and incorporates the capability to capture global spatio-temporal correlations. This enables the dynamic capture of long-range temporal correlations. Furthermore, the whale optimization algorithm is employed to efficiently design an optimal network structure that aligns with the requirements of the traffic domain and maximizes the utilization of limited computational resources. This design approach significantly enhances the model’s performance and improves the accuracy of traffic flow prediction. The experimental results on four real datasets demonstrate the efficacy of our approach. In PEMS03, it improves MAE by 2.6% and RMSE by 1.4%. In PEMS04, improvements are 1.6% in MAE and 1.4% in RMSE, with a similar MAPE score to the best baseline. For PEMS07, our approach shows a 4.1% improvement in MAE and 2.2% in RMSE. On PEMS08, it surpasses the current best baseline with a 3.4% improvement in MAE and 1.6% in RMSE. These results confirm the good performance of our model in traffic flow prediction across multiple datasets.

Revolutionizing Traffic Management: Advanced Machine Learning Techniques for Accurate Traffic Flow Prediction

In modern urban¬¬¬¬¬ transportation systems, efficient traffic flow prediction is of paramount importance to optimize traffic management, reduce congestion, and improve the overall commuting experience. This research report examines the application of state-of-the-art machine learning algorithms to accurately predict traffic flow patterns. By leveraging historical traffic data, weather conditions, time of day, and various other relevant features, our proposed model exhibits significant predictive capabilities. We study the effectiveness of various machine learning techniques, such as neural networks, decision trees, and ensemble methods, in capturing the complex dynamics of traffic flows. Through extensive experiments and validation using real datasets, we demonstrate the superiority of our approach compared to traditional methods. Ultimately, this research will contribute to the further development of intelligent transportation systems, paving the way for more efficient and sustainable urban mobility solutions

Incorporating environmental knowledge embedding and spatial-temporal graph attention networks for inland vessel traffic flow prediction

Accurate prediction of vessel traffic flow is crucial for maritime regulatory authorities and transportation planners. However, existing methods for inland vessel traffic flow prediction often overlook spatial correlation and environmental influences, leading to suboptimal accuracy. To address this issue, we propose an innovative model that incorporates environmental knowledge embedding and a spatial-temporal information extraction module. Our approach involves constructing a vessel traffic knowledge graph, embedding traffic flow through knowledge representation learning. The spatial-temporal information extraction module is leveraged to analyze inherent periodicity and external spatial relationships in vessel traffic flow. Extensive experiments on real-world datasets demonstrate that our approach significantly enhances predictive accuracy. In comparison to the second-ranked model, our approach achieves a decrease of 0.46 in mean absolute error, a decrease of 0.64 in root mean squared error, an increase of 3.06% in accuracy, and an increase of 0.07 in R-squared. Furthermore, our approach excels in upstream, downstream and long-term prediction, and displays robustness in handling noisy data.

Traffic Flow Prediction Research Based on an Interactive Dynamic Spatial–Temporal Graph Convolutional Probabilistic Sparse Attention Mechanism (IDG-PSAtt)

Accurate traffic flow prediction is highly important for relieving road congestion. Due to the intricate spatial–temporal dependence of traffic flows, especially the hidden dynamic correlations among road nodes, and the dynamic spatial–temporal characteristics of traffic flows, a traffic flow prediction model based on an interactive dynamic spatial–temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed. Specifically, the IDG-PSAtt model consists of an interactive dynamic graph convolutional network (IL-DGCN) with a spatial–temporal convolution (ST-Conv) block and a probabilistic sparse self-attention (ProbSSAtt) mechanism. The IL-DGCN divides the time series of a traffic flow into intervals and synchronously and interactively shares the captured dynamic spatiotemporal features. The ST-Conv block is utilized to capture the complex dynamic spatial–temporal characteristics of the traffic flow, and the ProbSSAtt block is utilized for medium-to-long-term forecasting. In addition, a dynamic GCN is generated by fusing adaptive and learnable adjacency matrices to learn the hidden dynamic associations among road network nodes. Experimental results demonstrate that the IDG-PSAtt model outperforms the baseline methods in terms of prediction accuracy. Specifically, on METR-LA, the mean absolute error (MAE) and root mean square error (RMSE) induced by IDG-PSAtt for a 60 min forecasting scenario are reduced by 0.75 and 1.31, respectively, compared to those of the state-of-the-art models. This traffic flow prediction improvement will lead to more precise estimates of the emissions produced by mobile sources, resulting in more accurate air quality forecasts. Consequently, this research will greatly support local environmental management efforts.

Machine Learning-Based Traffic Flow Prediction and Intelligent Traffic Management

With the rapid development of information technology, multiple time series forecasting, which is typical of traffic flow forecasting, has become increasingly important in big data analysis. As the cornerstone of intelligent transportation system, traffic flow forecasting has important scientific research value and practical application value for urban traffic operation scheduling, quality and efficiency improvement of logistics transportation industry and public travel planning. Traffic flow prediction is always an important task of intelligent transportation system. Due to the complex temporal and spatial dependence of traffic flow sequence, it is very challenging to construct accurate traffic flow prediction in its ring neural network, graph network and Transformer model. Much of the existing work is based on very good models. Considering the advantages of convolutional networks, such as high computational efficiency and strong feature extraction ability, a traffic flow prediction model based on multi-view spatiotemporal convolution is proposed.

A Variational Bayesian Inference-Based En-Decoder Framework for Traffic Flow Prediction

Accurate traffic flow prediction, a hotspot for intelligent transportation research, is the prerequisite for traffic flow prediction for making travel plans. The speed of traffic flow can be affected by roads condition, weather, holidays, etc. Moreover, sensors to catch the information about traffic flow will be interfered with by environmental factors such as illumination, collection time, occlusion, etc. Therefore, the traffic flow in the practical transportation system is complicated, uncertain, and challenging to predict accurately. Motivated from the aforementioned issues and challenges, in this paper, we propose a deep encoder-decoder prediction framework based on variational Bayesian inference. A Bayesian neural network is designed by combining variational inference with Gated Recurrent Units (GRU) which is used as the deep neural network unit of the encoder-decoder framework to mine the intrinsic dynamics of traffic flow. Then, the variational inference is introduced into the multi-head attention mechanism to avoid noise-induced deterioration of prediction accuracy. The proposed model achieves superior prediction performance on the Guangzhou urban traffic flow dataset over the benchmarks, particularly when the long-term prediction.

MVSTT: A Multiview Spatial-Temporal Transformer Network for Traffic-Flow Forecasting.

Accurate traffic-flow prediction remains a critical challenge due to complicated spatial dependencies, temporal factors, and unpredictable events. Most existing approaches focus on single-or dual-view learning and thus face limitations in systematically learning complex spatial-temporal features. In this work, we propose a novel multiview spatial-temporal transformer (MVSTT) network that can effectively learn complex spatial-temporal domain correlations and potential patterns from multiple views. First, we examine a temporal view and design a short-range gated convolution component from a short-term subview, and a long-range gated convolution component from a long-term subview. These two components effectively aggregate knowledge of the temporal domain at multiple granularities and mine patterns of node evolution across time steps. Meanwhile, in the spatial view, we design a dual-graph spatial learning module that captures fixed and dynamic spatial dependencies of nodes, as well as the evolution patterns of edges, from the static and dynamic graph subviews, respectively. In addition, we further design a spatial-temporal transformer to mine different levels of spatial-temporal features through multiview knowledge fusion. Extensive experiments on four real-world traffic datasets show that our method consistently outperforms the state-of-the-art baseline. The code of MVSTT is available at https://github.com/JianSoL/MVSTT.

Traffic Flow Prediction Based on Interactive Dynamic Spatio-Temporal Graph Convolution with a Probabilistic Sparse Attention Mechanism

Accurate traffic flow prediction is of great practical significance to alleviate road congestion. Existing methods ignore the hidden dynamic associations between road nodes, and for the problem of difficulty capturing the dynamic spatio-temporal features of traffic flow in the prediction process, a novel model based on the interactive dynamic spatio-temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed, which consists of an interactive dynamic graph convolutional network (IDGCN) structure with a spatio-temporal convolutional block (ST-Conv block) and a probabilistic sparse self-attention mechanism block (ProbSSAtt block). Among them, the IDGCN synchronizes the dynamic spatio-temporal features captured by interaction sharing, and the ST-Conv block is combined with the ProbSSAtt block to effectively capture the long short-term temporal features of the traffic flow. In addition, to effectively find the hidden dynamic associations between road network nodes, a dynamic graph convolutional network generated by the fusion of an adaptive neighbor matrix and a learnable neighbor matrix was constructed. Experimental results demonstrate that the prediction performance of the IDG-PSAtt model outperforms the baseline model under the evaluation criteria and experimental settings given in this paper. In the PEMS-BAY dataset, the mean absolute error and root mean square error of the IDG-PSAtt to 60 min are improved by 15.49% and 12.10%, compared with the state-of-the-art model, respectively.

Expressway traffic flow prediction based on MF-TAN and STSA.

Highly accurate traffic flow prediction is essential for effectively managing traffic congestion, providing real-time travel advice, and reducing travel costs. However, traditional traffic flow prediction models often fail to fully consider the correlation and periodicity among traffic state data and rely on static network topology graphs. To solve this problem, this paper proposes a expressway traffic flow prediction model based on multi-feature spatial-temporal adaptive periodic fused graph convolutional network (MFSTAPFGCN). First, we make fine preprocessing of the raw data to construct a complete and accurate dataset. Second, by deeply investigating the correlation properties among section speed, traffic flow, and section saturation rate, we incorporate these features into a multi-feature temporal attention mechanism in order to dynamically model the correlation of traffic flow in different time periods. Next, we adopt a spatial-temporal adaptive fusion graph convolutional network to capture the daily cycle similarity and potential spatial-temporal dependence of traffic flow data. Finally, the superiority of the proposed MFSTAPFGCN model over the traditional baseline model is verified through comparative experiments on real Electronic Toll Collection (ETC) gantry transaction data, and the effectiveness of each module is demonstrated through ablation experiments.

Gated Fusion Adaptive Graph Neural Network for Urban Road Traffic Flow Prediction

Accurate prediction of traffic flow plays an important role in maintaining traffic order and traffic safety, which is a key task in the application of intelligent transportation systems (ITS). However, the urban road network has complex dynamic spatial correlation and nonlinear temporal correlation, and achieving accurate traffic flow prediction is a highly challenging task. Traditional methods use sensors deployed on roads to construct the spatial structure of the road network and capture spatial information by graph convolution. However, they ignore that the spatial correlation between nodes is dynamically changing, and using a fixed adjacency matrix cannot reflect the real road spatial structure. To overcome these limitations, this paper proposes a new spatial-temporal deep learning model: gated fusion adaptive graph neural network (GFAGNN). GFAGNN first extracts long-term dependencies on raw data through stacking expansion causal convolution, Then the spatial features of the dynamics are learned by adaptive graph attention network and adaptive graph convolutional network respectively, Finally the fused information is passed through a lightweight channel attention to extract temporal features. The experimental results on two public data sets show that our model can effectively capture the spatiotemporal correlation in traffic flow prediction. Compared with GWNET-conv model on METR-LA dataset, the three indexes in the 60-minute task prediction improved by 2.27%,2.06% and 2.13%, respectively.

A Multi-Scale Residual Graph Convolution Network with hierarchical attention for predicting traffic flow in urban mobility

Accurate prediction of traffic flow is essential for optimizing transportation resource allocation and enhancing urban mobility efficiency. However, traffic data generated daily are vast and complex, involving dynamic and intricate changes in the traffic road network and traffic flow. Therefore, real-time and accurate prediction of traffic flow is a challenging task that requires modeling the intricate spatial–temporal dynamics of traffic data. In this paper, we propose a novel approach for traffic flow prediction, based on a Multi-Scale Residual Graph Convolution Network with hierarchical attention. First, we design a novel encoder–decoder with multi-independent channels to capture traffic flow information from different time scales and diverse temporal dependencies. Second, we employ a coupled graph convolution network with residual graph attention to dynamically learn the varying spatial features among and within traffic stations. Third, we utilize channel attention to fuse the multi-scale spatial–temporal dependencies and accurately predict traffic flow. We evaluate the proposed approach on multiple benchmark datasets, and the experimental results demonstrate its superior performance compared to state-of-the-art approaches in terms of various metrics.

Airspace situation analysis of terminal area traffic flow prediction based on big data and machine learning methods

Real-time and accurate prediction of terminal area arrival traffic flow is a key issue for terminal area traffic management. In this paper, we study the advantages and disadvantages of traditional dynamics-based prediction methods and time-series based prediction methods in the first step. Taking the advantages of the two type of methods, a terminal area arrival flow prediction framework based on airspace situation is proposed. In our method, the airspace situation is used as the machine learning feature to estimate the number of arrival aircraft. In addition, also based on machine learning approach, a correction stage is added to the algorithm to improve the accuracy of the prediction. ADS-B data collected from the terminal area of Chengdu is used to study the prediction accuracy based on different machine learning algorithms in the proposed framework. Experimental results show that the proposed method can predict the air traffic flow accurately. The average absolute error is only 0.35 aircraft/15 min, the root mean square error is 0.67 aircraft/15 min, and the maximum absolute error is 2 aircraft/15 min. Compared with the AOL method, our proposed method improves the accuracy of prediction by a margin of 90 % and 60 % according to the evaluation metrics of MAE and MAXAE, respectively.

GDENet: Graph Differential Equation Network for Traffic Flow Prediction

The accurate prediction of traffic flow is paramount for the advancement of intelligent transportation systems. Despite this, current prediction models only account for either temporal or spatial features in isolation, without considering their interaction, impeding the model’s ability to express itself. In light of this, we propose the graph differential equations network (GDENet), an approach that can effectively mine spatiotemporal correlation. Specifically, we propose a spatiotemporal feature integrator (STFI), which alleviates the error caused by the deviation of the sampling distribution from the overall distribution. By incorporating temporal information into the model for training and combining it with spatial features, we thoroughly explore the spatiotemporal intrinsic association. When compared to state-of-the-art methods, our proposed algorithm reduces memory consumption and elevates computational efficiency and the practical value. We conduct experiments with real-world datasets, and our proposed model outperformed advanced prediction models.

A multi-modal attention neural network for traffic flow prediction by capturing long-short term sequence correlation

Accurate traffic flow prediction information can help traffic managers and drivers make more rational decisions and choices. To make an effective and accurate traffic flow prediction, we need to consider not only the spatio-temporal dependencies between data, but also the temporal correlation between data. However, most existing methods only consider temporal continuity and ignore temporal correlation. In this paper, we propose a multi-modal attention neural network for traffic flow prediction by capturing long-short term sequence correlation (LSTSC). In the model, we employed attention mechanisms to capture the spatio-temporal correlations of the sequences, and the model based on multiple decision forms demonstrated higher accuracy and reliability. The superiority of the model is demonstrated on two datasets, PeMS08 and PeMSD7(M), particularly for long-term predictions.

Performance analysis of machine learning methods for short-term traffic prediction

With the rapid development of urbanization and the rapid increase of the number of motor vehicles, the problem of urban traffic congestion has become increasingly prominent. The accurate prediction of short-term traffic flow is considered as a promising solution, which can provide a key decision-making basis for route planning and traffic flow scheduling, so that can greatly alleviate or even prevent congestion. Researchers have used many machine learning methods to predict traffic flow, but few people pay attention to the boundaries of different machine algorithms. In this paper, we use AdaBoost, Random Forest, SVM and BP neural network to predict short-term traffic flow in California, which aims to compare the differences in prediction performance of different algorithms and analyze their potential reasons. The results show that, the integration methods such as AdaBoost and Random Forest are quite appropriate to solve the short-term traffic flow, which can obtain an accuracy more than 95%, while prediction made by SVM is less precise than the two aforementioned methods with a 79% accuracy. And BP neural network may be inappropriate if the parameters remain default. The different results are due to the periodicity of the database. Integration methods can recognize the periodicity while the SVM and BP neural network fail to do it. When employing the SVM and BP neural network, the datasets need to be divided within a period to avoid being disturbed by cyclically. Besides, the precise of BP neural network can be improved when adjusting the parameters to the optimal.

Short-term traffic flow prediction based on weather factors analysis and neural network

With the progress of urban transportation infrastructure and the construction of intelligent transportation system, sufficiently accurate traffic flow prediction is of increasing importance for urban planning and management. This paper based on traffic flow data in a section of highway in Los Angeles, USA, we combine external qualitative data such as weather and work calendars, analyze and process the factors significantly influencing traffic flow first, then train and test the traffic flow data by building machine learning Support Vector Regression (SVR) model and the Back Propagation neural network (BP neural network). The test results show that the BP neural network traffic flow prediction model incorporating the external environment is more accurate, and that the external environment has a significant impact on the traffic flow prediction problem. If more significantly influencing factors can be taken into account, or if the current algorithm can be optimized, the prediction accuracy will have further improved, thus effectively enhancing the efficiency and safety of traffic systems.

TRELM-DROP: An impavement non-iterative algorithm for traffic flow forecast

Accurate prediction of traffic flow is crucial to building a smart city. Given the nonlinearity of traffic flow, this paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence combined with the DROP strategy. The algorithm is referred to as TRELM-DROP. The Tent chaos strategy and residual correction method reduce the impact of randomness in traffic flow. On this basis, the Tent and residual correction strategy avoids the weight optimization of the ELM algorithm using the iterative method. A DROP strategy is proposed in the proposed algorithm to improve its ability to predict traffic flow under varying conditions. A comprehensive comparison of 36 real-world datasets is presented in this paper, comparing TRELM-DROP with other benchmark models. The results show that the proposed algorithm can produce the best prediction performance regarding various prediction error metrics under various traffic conditions without iterative optimization.

Spatial-temporal Traffic Flow Prediction Model Based on the GAT and BiGRU

Real-time and accurate traffic flow prediction is crucial for improving the safety, stability, and efficiency of intelligent transportation system. Considering that traffic flow prediction methods rarely analyze from the perspective of the road network, in this paper, a spatial-temporal traffic flow prediction model based on the combination of graph attention network (GAT) and bidirectional gated recurrent unit (BiGRU) neural network is proposed. Firstly, GAT is used to analyze the complex topology of the road network, effectively obtaining the spatial features of the road network. Secondly, BiGRU is used to learn the dynamic changes of traffic flow data, effectively obtaining the temporal features. Thirdly, the obtained spatial-temporal features are output by the fully connected layer to complete the prediction of future traffic flow. Finally, the model is validated and evaluated on the California highway dataset. The experimental results show that the accuracy of GAT-BiGRU model is better than other benchmark models in predicting future traffic flows transformation, especially in long-term prediction.

Privacy-Aware Traffic Flow Prediction Based on Multi-Party Sensor Data with Zero Trust in Smart City

With the continuous increment of city volume and size, a number of traffic-related urban units (e.g., vehicles, roads, buildings, etc.) are emerging rapidly, which plays a heavy burden on the scientific traffic control of smart cities. In this situation, it is becoming a necessity to utilize the sensor data from massive cameras deployed at city crossings for accurate traffic flow prediction. However, the traffic sensor data are often distributed and stored by different organizations or parties with zero trust, which impedes the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it requires challenging efforts to balance the trade-off between data sharing and data privacy to enable cross-organization traffic data fusion and prediction. In light of this challenge, we put forward an accurate LSH (locality-sensitive hashing)-based traffic flow prediction approach with the ability to protect privacy. Finally, through a series of experiments deployed on a real-world traffic dataset, we demonstrate the feasibility of our proposal in terms of prediction accuracy and efficiency while guaranteeing sensor data privacy.

TransFusion Model Fusion Mechanism Based on Transformer for Traffic Flow Prediction

In recent years, the problem of traffic congestion has become a hot topic. Accurate traffic flow prediction methods have received extensive attention from many researchers all over the world. Although many methods proposed at present have achieved good results in the field of traffic flow prediction, most of them only consider the static characteristic of traffic data, but do not consider the dynamic characteristic of traffic data. The factors that affect traffic flow prediction are changeable, and they will change over time. In response to this dynamic characteristic, the authors propose a model fusion mechanism based on transformer (TransFusion). The authors adopt two basic forecasting models (TCN and LSTM) as the underlying architectures. In view of the performance of different models on the traffic data at different times, the authors design a model fusion mechanism to assign dynamic weights to basic models at different times. Experiments on three datasets have proved that TransFusion has a significant improvement compared with basic models.